So far, scientists and researchers have made claims on behalf of AI-enabled technologies, but they have not really been tested in large-scale market applications. We will see a lot of those technologies put into marketplace practice for the users to judge and evaluate. For starters, let's review some recent industry statistics. From chatbots and digital agents in CRM to virtual reality (VR)-powered shop-floor demos, AI has promised something for every industry sector. McKinsey & Company, in Notes from the Frontier: Modeling the Impact of AI on the World Economy, has predicted that by 2030, 70 percent of businesses will use AI.

Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters.

Tacotron c. Tacotron2 4. Our work: Japanese Tacotron 5. Implementation 3 4. TTS architecture: traditional pipeline - Typical pipeline architecture for statistical parametric speech synthesis - Consists of task-specific models - linguistic model - alignment (duration) model - acoustic model - vocoder 4 5. TTS architecture: End-to-end model - End-to-end model directly converts text to waveform - End-to-end model does not require intermediate feature extraction - Pipeline models accumulate errors across predicted features - End-to-end model's internal blocks are jointly optimized 5 6. [1] Wei Ping, Kainan Peng, Jitong Chen: ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech. End-to-end model: Decoding with attention mechanism 10 Time step 2. - The previous output is fed back to next time step - Assign alignment probabilities to encoded inputs 11.

So, you want to scale your computing muscle to train bigger deep learning models. Can your data center handle it? According to Nvidia, which sells more of the specialized chips used in machine learning than any other company, it most likely cannot. These systems often consume so much power, a conventional data center doesn't have the capacity to remove the amount of heat they generate. It's easy to see how customers without infrastructure that can support a piece of Nvidia hardware is a business problem for Nvidia.

Along America's west coast, the world's most valuable companies are racing to make artificial intelligence smarter. Google and Facebook have boasted of experiments using billions of photos and thousands of high-powered processors. But late last year, a project in eastern Tennessee quietly exceeded the scale of any corporate AI lab. It was run by the US government. The record-setting project involved the world's most powerful supercomputer, Summit, at Oak Ridge National Lab.

The combinations of joint atomic actions, which quickly becomes explosion in the number of discrete actions can intractable when M increases. However, a simple fix be efficiently addressed by a policy with factorized is to represent the joint distribution over discrete actions as distribution across action dimensions. We factorized across dimensions, so that the joint policy is still show that the discrete policy achieves significant tractable. As prior works have applied such discretization performance gains with state-of-the-art on-policy method in practice (OpenAI, 2018; Jaśkowski et al., 2018), optimization algorithms (PPO, TRPO, ACKTR) we aim to carry out a systemic study of such straightforward especially on high-dimensional tasks with complex discretization method in simulated environments, and show dynamics. Additionally, we show that an ordinal how they improve upon on-policy optimization baselines.

We propose a method for tackling catastrophic forgetting in deep reinforcement learning that is \textit{agnostic} to the timescale of changes in the distribution of experiences, does not require knowledge of task boundaries, and can adapt in \textit{continuously} changing environments. In our \textit{policy consolidation} model, the policy network interacts with a cascade of hidden networks that simultaneously remember the agent's policy at a range of timescales and regularise the current policy by its own history, thereby improving its ability to learn without forgetting. We find that the model improves continual learning relative to baselines on a number of continuous control tasks in single-task, alternating two-task, and multi-agent competitive self-play settings.

Noname manuscript No. (will be inserted by the editor) Abstract Selecting an optimal set of icons is a crucial step in the pipeline of visual design to structure and navigate through content. However, designing the icons sets is usually a difficult task for which expert knowledge isrequired. In this work, to ease the process of icon set selection to the users, we propose a similarity metric which captures the properties of style and visual identity. We train a Siamese Neural Network with an online dataset of icons organized in visually coherent collections that are used to adaptively sample training data and optimize the training process. As the dataset contains noise, we further collect human-rated information onthe perception of icon's similarity which will be used for evaluating and testing the proposed model. We present several results and applications based on searches, kernel visualizations and optimized set proposals thatcan be helpful for designers and non-expert users while exploring large collections of icons. Keywords Iconography · Illustration · Visualization · Appearance Similarity · Machine Learning 1 Introduction Visual communication is one of the most important ways to share and transmit information [34,33]. In the same way as words are used for verbal communication, symbols or icons are the elements used to convey information ina universal and ubiquitous language [1, 20]. Icons are key elements to structure visual content and make it more appealing and comprehensible. Style and visual identity are preserved for each collection.

Spatial time series forecasting problems arise in a broad range of applications, such as environmental and transportation problems. These problems are challenging because of the existence of specific spatial, short-term and long-term patterns, and the curse of dimensionality. In this paper, we propose a deep neural network framework for large-scale spatial time series forecasting problems. We explicitly designed the neural network architecture for capturing various types of patterns. In preprocessing, a time series decomposition method is applied to separately feed short-term, long-term and spatial patterns into different components of a neural network. A fuzzy clustering method finds cluster of neighboring time series based on similarity of time series residuals; as they can be meaningful short-term patterns for spatial time series. In neural network architecture, each kernel of a multi-kernel convolution layer is applied to a cluster of time series to extract short-term features in neighboring areas. The output of convolution layer is concatenated by trends and followed by convolution-LSTM layer to capture long-term patterns in larger regional areas. To make a robust prediction when faced with missing data, an unsupervised pretrained denoising autoencoder reconstructs the output of the model in a fine-tuning step. The experimental results illustrate the model outperforms baseline and state of the art models in a traffic flow prediction dataset.

Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LVQ) models against adversarial attacks has however not yet been studied to the same extend. We therefore present an extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods. In contrast to this, Generalized Matrix LVQ shows a high susceptibility to adversarial attacks, scoring consistently behind all other models. Additionally, our numerical evaluation indicates that increasing the number of prototypes per class improves the robustness of the models.